





"""simple docstring"""

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__UpperCAmelCase        =4
__UpperCAmelCase        =3


class       lowerCAmelCase__				(   UpperCAmelCase_  ):
 pass





def        __a							(			A )       ->						Tuple:



   '''simple docstring'''



   for shard in shards:
      for i in range(_A ):
         yield {"i": i, "shard": shard}



def        __a							(			)       ->						str:



   '''simple docstring'''



   A__											=					int(os.environ["RANK"] )
   A__											=					int(os.environ["WORLD_SIZE"] )

   A__											=					ArgumentParser()
   parser.add_argument("--streaming" ,	type=_A )
   parser.add_argument("--local_rank" ,	type=_A )
   parser.add_argument("--num_workers" ,	type=_A ,	default=0 )
   A__											=					parser.parse_args()
   A__											=					args.streaming
   A__											=					args.num_workers

   A__											=					{"shards": [f"""shard_{shard_idx}""" for shard_idx in range(_A )]}
   A__											=					IterableDataset.from_generator(_A ,	gen_kwargs=_A )
   if not streaming:
      A__											=					Dataset.from_list(list(_A ) )

   A__											=					split_dataset_by_node(_A ,	rank=_A ,	world_size=_A )
   A__											=					torch.utils.data.DataLoader(_A ,	num_workers=_A )

   A__											=					NUM_SHARDS * NUM_ITEMS_PER_SHARD
   A__											=					full_size // world_size
   expected_local_size += int(rank < (full_size % world_size) )

   A__											=					sum(1 for _ in dataloader )
   if local_size != expected_local_size:
      raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}""" )


if __name__ == "__main__":
 main()



from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Tuple 			=		CustomTokenizer
			pass


